Median filter for liquid chromatography-mass spectrometry data

ABSTRACT

High-intensity, spiked noise is reduced in chromatography-mass spectrometry data by applying a nonlinear filter such as a moving median filter to the data. The filter is applied to individual mass chromatograms, plots of ion abundance versus retention time for each detected mass-to-charge ratio, and the filtered chromatograms are combined to form a filtered total ion current chromatogram. Standard linear filters are not effective for reducing noise in liquid chromatography-mass spectrometry (LC-MS) data because they assume a normal distribution of noise. LC-MS noise, however, is not normally distributed.

RELATED APPLICATIONS

This application is a continuation of patent application Ser. No.09/994,575, “Median Filter for Liquid Chromatography-Mass SpectrometryData,” filed Nov. 27, 2001, which claims the benefit of U.S. ProvisionalApplication 60/253,178, “Informatics System,” filed Nov. 27, 2000 andU.S. Provisional Application No. 60/314,996, “Nonlinear Filter ForLiquid Chromatography-Mass Spectrometry Data,” filed Aug. 24, 2001, allof which are herein incorporated by reference.

FIELD OF THE INVENTION

This invention relates generally to analysis of data collected byanalytical techniques such as chromatography and spectrometry. Moreparticularly, it relates to a nonlinear filter such as a moving medianfilter for noise reduction in mass chromatograms acquired by liquidchromatography-mass spectrometry.

BACKGROUND OF THE INVENTION

Liquid chromatography-mass spectrometry (LC-MS) is a well-known combinedanalytical technique for separation and identification of chemicalmixtures. Chromatography separates the mixture into its constituentcomponents, and mass spectrometry further analyzes the separatedcomponents for identification purposes.

In its basic form, chromatography involves passing a mixture dissolvedin a mobile phase over a stationary phase that interacts differentlywith different mixture constituents Components that interact morestrongly with the stationary phase move more slowly and therefore exitthe stationary phase at a later time than components that interact morestrongly with the mobile phase, providing for component separation. Adetector records a property of the exiting species to yield atime-dependent plot of the property, e.g., mass or concentration,allowing for quantification and, in some cases, identification of thespecies. For example, an ultraviolet (UV) detector measures the UVabsorbance of the exiting analytes over time. When liquid chromatographyis coupled to mass spectrometry, mass spectra of the eluting componentsare obtained at regular time intervals for use in identifying themixture components. Mass spectra plot the abundance of ions of varyingmass-to-charge ratio produced by ionizing and/or fragmenting the elutedcomponents. The spectra can be compared with existing spectral librariesor otherwise analyzed to determine the chemical structure of thecomponent or components. Note that LC-MS data are two-dimensional; thatis, a discrete data point (intensity) is obtained for varying values oftwo independent variables, retention time and mass-to-charge ratio(m/z).

LC-MS data are typically reported by the instrument as a total ioncurrent (TIC) chromatogram, the sum of all detected ions at each scantime. Peaks in the chromatogram represent separated components of themixture eluting at different retention times. A noise-free chromatogram10, shown in FIG. 1A, appears as a series of smooth peaks 12 a-12 c,each extending over multiple scan times. As shown in the TICchromatogram of FIG. 1B, however, LC-MS data often have high-intensitynoise spikes 14 a-14 d superimposed on the peaks. Although componentselute over multiple scans, noise spikes typically do not extend beyondone scan time. If the TIC chromatogram has little noise, an operator candetermine the total number of peaks and then examine each peak'scorresponding mass spectrum to identify the eluted species. However, asthe amount of noise present increases, it becomes more difficult for theoperator to distinguish the chromatographic peaks, particularly if thenoise level is higher than the signal level. In such cases, the operatoris left to examine each individual mass spectrum manually, select themass-to-charge ratios corresponding to known or likely mixturecomponents, and then assemble a reduced total ion current chromatogramfrom the selected masses only. Such a procedure is clearly very timeconsuming. Furthermore, when the mixture contains unknown analytes, theoperator cannot confidently determine which mass spectral peaks arenoise and which are actual peaks. Thus the only recourse the operatorhas is to adjust various instrument parameters and repeat the experimentwith a different sample, hoping for less noise in the resultingchromatogram.

Because it enables the identification and quantification of hundreds tothousands of analytes in a single injection, LC-MS is currently beingused to analyze complex biological mixtures (see, e.g., D. H. Chace etal., “Mass Spectrometry in the Clinical Laboratory,” Chem. Rev. 101(2001): 445-477). Proteomics is a relatively new field that aims todetect, identify, and quantify proteins to obtain biologically relevantinformation. Both proteomics and metabolomics (the detection,identification, and quantification of metabolites and other smallmolecules such as lipids and carbohydrates) may facilitate diseasemechanism elucidation, early detection of disease, and evaluation oftreatment. Recent advances in mass spectrometry have made it anexcellent tool for structural determination of proteins, peptides, andother biological molecules. However, proteomics and small moleculestudies typically have a set of requirements that cannot be met bymanual interpretation of the LC-MS data.

First, these studies require high-throughput analysis of small volumesof biological fluid. Manual data interpretation creates a bottleneck insample processing that severely limits the number of samples that can beanalyzed in a given time period. Furthermore, while large availablesample volumes allow an operator to adjust parameters by trial and errorto obtain adequate chromatograms and spectra, biological samples areavailable in such small volumes that it is imperative to extract usefulinformation from all of the available sample. Second, unlike traditionalresearch applications, in which a relatively small amount of data isrequired, the paradigm of these studies is to acquire enormous amountsof data and then mine the data for new correlations and patterns. Manualdata analysis is therefore unfeasible. In addition, biological samplesare generally complex mixtures of unknown compounds, and so it is notdesirable to extract only known spectra and discard the remaining data,an approach that has been used for studies involving quantification ofknown compounds in a mixture. Finally, LC-MS instruments produce anenormous amount of data: a single one-hour chromatographic run canproduce up to 80 MB of binary data. For storage and subsequent datamining purposes, it is highly desirable to reduce the amount of data toretain information while discarding noise. To satisfy theserequirements, a data analysis method is needed that can acquire a largeamount of data from low-volume biological mixtures, extract usefulinformation from the resulting noisy data set, and identify unknowncompounds from the extracted information. An essential component of sucha method is the ability to filter noise accurately so that peaks can bedistinguished automatically.

The problem of filtering chromatographic noise has been addressed tovarious degrees in the prior art. The component detection algorithm(CODA) is an automated method for selecting mass chromatograms with lownoise and low background. CODA is described in W. Windig et al., “ANoise and Background Reduction Method for Component Detection in LiquidChromatography/Mass Spectrometry,” Anal. Chem., 68 (1996): 3602-3606.The method computes a smoothed and mean-subtracted version of each masschromatogram, compares it with the original chromatogram, and calculatesa similarity index between the two. Chromatograms whose similarity indexexceeds a threshold value are retained and combined to form a reducedtotal ion chromatogram, while other chromatograms are rejected. CODA hasproven very effective at selecting high-quality mass chromatograms.However, it can only accept or reject entire chromatograms based ontheir noise level, but cannot filter noise from an individualchromatogram. As a result, noisy chromatograms that contain usefulinformation are eliminated, and important peaks may not be detected.

Techniques exist for filtering noise and background from spectrometricdata. For example, U.S. Pat. No. 5,995,989, issued to Gedcke et al.,describes a filtering method in which an average background level and anaverage deviation from the background are computed and used to define alocal threshold for each data point. Points exceeding the threshold areretained, while points below the threshold are considered to be noiseand discarded. The technique described in Gedcke et al. is onlyeffective for noise levels that are substantially below the level of thepeaks. For data such as that illustrated in FIG. 1B, high-intensitynoise spikes cannot be removed using the disclosed method.

In U.S. Pat. No. 6,112,161, issued to Dryden et al., a method forenhanced integration of chromatography or spectrometry signals isdescribed. A baseline signal is computed from a moving average of theactual signal. The difference between the baseline and actual signal isa baseline-adjusted signal containing peaks and high-frequency noise. Anintensity range of the noise is determined, and all signal outside ofthis range is considered to be peaks, while signal inside this range isconsidered to be noise. As with the method of Gedcke et al., the methodof Dryden et al. can only be used when the noise intensity issubstantially lower than the signal intensity. Because LC-MS data oftenhas noise values exceeding the signal values, the method of Dryden etal. is not effective at removing noise from LC-MS data.

A moving median digital filter has been used to remove noise from massspectrometry and potentiometric titration data, as described in C. L. doLago et al., “Applying moving median digital filter to mass spectrometryand potentiometric titration,” Anal. Chim. Acta, 310 (1995): 281-288.Each data point is replaced by the median of the values in a windowsurrounding the point. With respect to the mass spectrometry data, thefilter is applied both to the electron multiplier output, i.e., the ionabundance values, and to the magnetic field sensor, i.e., themass-to-charge ratio. The method is not, however, applied totwo-dimensional data such as LC-MS data. In most cases, state-of-the-artLC-MS instruments do not report the mass spectra as continuous smoothpeaks, but rather as centroided data, i.e., single-mass peaks at theaverage mass value of the true peak. Without centroiding, anunmanageable amount of data would be generated for each spectrum. Amoving median filter applied to centroided mass spectral data wouldremove peaks and noise equally. Because the peak shape is removed in thereported data, filtering or analytical methods cannot be applied to themass spectra. Moreover, in some cases, one major source of noise,detector noise, can corrupt an entire mass spectrum. If a high fractionof the points in the filter window are corrupted, then a median filterapplied to the spectrum cannot remove this noise.

There is still a need, therefore, for a method for removing noise,particularly high-intensity spikes, from chromatographic andspectrometric data such as LC-MS data.

SUMMARY OF THE INVENTION

The present invention provides a method for filtering noisy masschromatograms in two-dimensional liquid chromatography-mass spectrometry(LC-MS) data. The method can remove noise spikes that have a higherintensity than peaks corresponding to eluted analytes, while essentiallyretaining the peak intensity and shape. It therefore performssubstantially better than conventional linear filters that assume anormal distribution of the noise.

In a method of the invention for characterizing a chemical or biologicalsample, a series of mass spectra are generated by chromatography (e.g.,liquid chromatography) and mass spectrometry. A total ion current (TIC)chromatogram is obtained from the mass spectra, and a median filter isapplied to the chromatogram, resulting in a filtered total ionchromatogram with lower noise than the original chromatogram.Preferably, individual chromatograms are generated from the series ofmass spectra, and the filter is applied separately to each individualchromatogram. The total ion current chromatogram is then reconstructedfrom the individual filtered chromatograms. Alternatively, the raw massspectral data can be compared with the filtered chromatograms and theraw data replaced with the corresponding filtered data if the filtereddata has a lower intensity value. The TIC chromatogram can then beassembled from the thresholded raw data. Subsequent to filtering,additional post-acquisition processing steps can be performed, such asapplying a component detection algorithm to the filtered data to selectrelatively noise-free individual chromatograms.

The filter can be any suitable median filter such as a moving medianfilter or modified median filter, and the method preferably alsoincludes selecting and optimizing one or more parameters of the filter.For example, the parameter can be selected based on the scan rate of themass spectrometer or on subsequent data analysis, such as peakselection, of the mass spectra.

Also provided by the present invention is a program storage deviceaccessible by a processor and tangibly embodying a program ofinstructions executable by the processor to perform method steps for theabove method.

BRIEF DESCRIPTION OF THE FIGURES

FIGS. 1A-1B are schematic diagrams of a noise-free total ion currentchromatogram and a noisy total ion current chromatogram, respectively,as known in the prior art.

FIGS. 2A-2B are histograms of LC-MS spectral intensities of afive-protein mixture and a high-molecular weight human serum fraction,respectively.

FIGS. 3A-3B illustrate a moving median filter of the invention appliedto a chromatogram peak and to a chromatogram noise spike, respectively.

FIG. 3C illustrates a moving average filter applied to a chromatogramnoise spike.

FIG. 4 is a flow diagram of a median filter method of the presentinvention.

FIG. 5 illustrates the application of a moving median filter with apoorly chosen window size to a chromatogram peak.

FIGS. 6A-6B show total ion chromatograms, base peak traces, andtwo-dimensional LC-MS data obtained from an LC-MS experiment of aproteolytic digest of human serum, before and after application of themoving median filter of the invention, respectively.

FIG. 6C shows the total ion chromatogram, base peak trace, andtwo-dimensional LC-MS plot of FIG. 6A after application of a meanfilter.

FIG. 7 is a block diagram of a hardware system for implementing themethod of FIG. 4.

DETAILED DESCRIPTION OF THE INVENTION

The present invention provides a method for filtering chromatographicand spectrometric data to reduce noise in individual mass chromatograms,thereby facilitating subsequent selection of peaks or high qualitychromatograms for component detection. In liquid chromatography-massspectrometry (LC-MS) or gas chromatography-mass spectrometry (GC-MS)data, the noise intensity is often larger than the intensity of thepeaks corresponding to eluted species, making it very difficult toextract meaningful information from the data. The method of theinvention is able to reduce substantially such large magnitude noisespikes.

LC-MS noise originates from a variety of sources corresponding todifferent components of the system. Each physical mechanism of thesystem contributes its own noise distribution to the final measured ioncurrent. For example, chemical noise results from column bleed, i.e.,long-time elution of strongly-adsorbed species at particularmass-to-charge ratios, low-concentration sample contaminants, anddetection of the chromatographic mobile phase. In the mass spectrometer,the ion generation, selection, and detection processes all generatenoise. Electronic signal processing and analog-to-digital conversion addnoise to the acquired data. The noise sources and distributions are notwell understood for all components, making it difficult to select anappropriate filter.

In order to understand the noise distribution in LC-MS data better, thepresent inventor has prepared histograms of non-normalized intensitydistributions of LC-MS spectra of two samples. FIG. 2A is a histogram oflog intensity for a tryptic digest of a five-protein mixture (equalamounts by mass of horse myoglobin, bovine RNAse A, bovine serumalbumin, bovine cytochrome C, and human hemoglobin). FIG. 2B is ahistogram of log intensity for a tryptic digest of a high-molecularweight fraction of human serum produced by ultrafiltration through a 10kD cutoff membrane. In both cases, the samples were reduced withdithiothreitol and carboxymethylated using iodoacetic acid and sodiumhydroxide prior to digestion. The two samples display similardistributions, one at relatively low intensity and the other at higherintensity. In general, the major noise component of LC-MS data ischemical. It is believed that the second distribution, at lower ioncurrent intensities, is associated with electrical noise. Bothdistributions appear close to normal in the log scale.

In methods of the invention, a digital median filter, a nonlinearfilter, is applied to individual mass chromatograms to reduce the noiselevel. A mass chromatogram is a plot of intensity versus retention timefor a particular range of mass-to-charge ratio of detected ions. Anonlinear filter applies a nonlinear function to the data to befiltered. A nonlinear filter such as a median filter is particularlywell-suited to LC-MS data because of the noise distributioncharacteristics of this type of data. As recognized by the presentinventor, noise in LC-MS data is not normally distributed in the linearscale, i.e., does not follow a Gaussian distribution. Additionally,empirical study of LC-MS data has revealed that in individual masschromatograms, noise spikes typically occur over a single scan timeonly. Standard filtering techniques for LC-MS data use moving averagefilters, which are linear filters and therefore effective at removingonly normally distributed noise. Note that while noise may be correlatedin adjacent points along the mass axis of a mass spectrum, it istypically not as highly correlated along the time axis. Thus there is noguarantee that noise reduction methods developed for one of thedimensions can be extended to the other dimension.

A simple median filter used in the preferred embodiment of the inventionis a moving median filter, illustrated in FIGS. 3A-3B. A moving medianfilter replaces each point with the median of the points in a window ofa given size centered on the selected point. For example, a three-pointwindow examines a selected point and the neighboring point on each sideof the selected point. Moving median filters are used for noisesuppression in image processing but, to the knowledge of the presentinventor, have not previously been applied to chromatographic data. FIG.3A illustrates the application of a three-point moving median filter toa smooth chromatographic peak extending over multiple MS scan times. Thetop plot is the raw data, and the bottom plot is the filtered data.Points on the peak side slopes are necessarily the median of the threevalues in the window, and do not change upon application of the filter.The highest point of the peak is replaced by the larger of the twoneighboring values. Thus the moving median filter flattens the peakslightly.

FIG. 3B illustrates the effect of the same three-point moving medianfilter on a single-point noise spike. The points surrounding the spikechange little, if at all, but the noise spike is replaced by the higherof its two adjacent points, i.e., is completely removed. FIGS. 3A-3Bhighlight the benefits of a moving median filter: it removeshigh-intensity noise spikes while retaining the sharpness of peak edges.In general, the filter removes features narrower than its half-widthwhile retaining features wider than its width. A three-point movingaverage filter applied to the same noise spike is illustrated in FIG.3C. In this case, each point is replaced by the mean of itself and itstwo surrounding points. The points at the edge of the noise spike areincreased in value, while the spike itself is reduced significantly, butis still present. If the noise is of larger magnitude than the actualpeaks, then the filtered noise is comparable to the peaks, and thefilter is not effective in reducing noise.

A flow diagram of a method 20 of the invention for reducing noise inLC-MS or GC-MS data is shown in FIG. 4. First, in step 22, thetime-dependent mass spectra are acquired. Next, in step 24, a masschromatogram is generated for each integer mass in the entire set ofmass spectra. For example, peaks at masses of 1321.7 and 1322.1 aresummed and combined into the mass chromatogram for an integer mass of1322. Alternatively, data points can be combined into mass ranges thatdo not necessarily correspond to integer values. In step 26, the medianfilter is applied to each mass chromatogram generated in step 24. Next,in an optional step 28, a component selection algorithm such as CODA isapplied to the filtered mass chromatograms.

Finally, the filtered mass chromatograms are combined into a reduced orfiltered total ion current chromatogram in step 30. One method is simplyto sum the intensities at each time point. Alternatively, the raw massspectral data obtained in step 22 can be thresholded using the filteredchromatograms. To do this, each raw data point is compared with itscorresponding point in the filtered chromatograms. Recall that the rawdata contain points at non-integer values of mass-to-charge ratio, whilethe filtered chromatograms contain points corresponding to ranges ofmass values. If the intensity value of the raw data exceeds the value ofthe corresponding filtered point, then the original point is replaced bythe filtered value. If not, it is retained.

The method 20 is typically implemented as part of an automated dataanalysis method for two-dimensional LC-MS or GC-MS. Data filteredaccording to the present invention may be subjected to, for example,peak recognition algorithms and structural identification algorithms. Itis anticipated that the filtered data can be much more successfullyanalyzed by subsequent algorithms than can unfiltered data. In fact, oneof the problems with the CODA method is that it removes noisychromatograms, thereby also removing any information contained withinthe chromatograms.

Although the method 20 is best implemented by applying the median filterto the individual mass chromatograms and then combining the filteredchromatograms into a reduced total ion current chromatogram, the filteralternatively can be applied directly to the original total ion currentchromatogram. In individual mass chromatograms, noise spikes typicallyoccur over a single scan time only and are therefore effectivelyfiltered using a moving median filter of the invention. In the total ioncurrent chromatogram, however, spikes that occur at different masses butadjacent retention times can effectively merge to extend over multiplescan times and therefore pass the filter.

The median filters used in step 26 have parameters that are adjusted toachieve optimal filtering of the signal. The moving median filter, forexample, has one parameter, the window size, the number of points overwhich the median is computed. The optimal window size is determined by anumber of factors including the typical peak width and the scan rate.The peak width of LC-MS or GC-MS data varies with column conditions,flow rate, and mobile and stationary phases, among other factors. Thewindow size should not be wider than the typical peak width, or peakswill be significantly distorted. FIG. 5 illustrates the use of a movingmedian window that is larger than the peak width. As shown, the peakbase is approximately five points wide, while the filter window is ninepoints wide. The peak is essentially removed, and so this filter widthis unacceptable. The filter width must be decreased to three pointsbefore the peak can survive the filter substantially unchanged.

In addition, the scan rate determines the density of points in thechromatogram and therefore also affects the optimal window size. Ifscans are performed half as frequently as in the chromatogram of FIG.3A, all else being equal, the peak contains fewer points and therefore asmaller window is needed to retain the peak while eliminating noisespikes. From the point of view of the present invention, therefore, itis desirable to scan more frequently, assuming the increased samplingdoes not result in degraded signal-to-noise ratios in the individualscans. In a preferred embodiment, the method derives an expected peakwidth from the chromatography parameters and resolution and then selectsa window size based on the expected peak width.

Additionally, the optimal parameters are determined by the quality ofthe resulting reduced total ion chromatogram and the ease and accuracywith which subsequent component detection or automated peak picking canbe performed.

In some embodiments, an adaptive window size is employed. The windowsize is not the same for all data points, but varies based on a numberof factors. The window size can be selected based on characteristics ofthe data by analyzing subsets of points in each mass chromatogram.Alternatively, the variation in window size can be predetermined basedon knowledge of the instrument conditions. If peaks at later retentiontimes are known to be broader than peaks at earlier times, then thewindow is preset to increase with retention time.

Additional median filters include a modified median filter. This filterhas more parameters than the moving median filter, but the parametersare optimized based on the same principles.

Methods of the invention preferably use standard algorithms forimplementing the various steps. For example, the moving median filter isapplied using existing techniques for obtaining the median of a set ofpoints. In one such algorithm, the median window is applied sequentiallyto the data beginning at the lowest-time data point. Points within thewindow are ordered and the central point selected as the median. Forsubsequent points, the earliest-time point is removed and the new pointinserted into the correct position in the ordered set. At the edges ofthe data set, additional points are appended so that the window can becentered on the first and last points. Preferably, the additional pointshave the same values as the edge points.

An example application of the method is shown in FIGS. 6A and 6B. FIG.6A shows a total ion current chromatogram, base peak trace, andtwo-dimensional plot acquired from an LC-MS experiment of a proteolyticdigest of human serum. The darkness of each point in the two-dimensionalplot corresponds to the detected intensity at that mass-to-charge ratioand retention time. Each point in the TIC is the sum of all pointsdirectly below it in the two-dimensional plot, while each point in thebase peak trace is the maximum value of all points below it. A movingmedian filter with a window size of seven points was applied to the masschromatograms extracted from the data. FIG. 6B shows the resultingfiltered data. FIG. 6C shows the results of applying a seven-point meanfilter to the same data. Note that the mean filter changes the data verylittle, while the median filter clearly brings out six smooth peaks.

Although not limited to any particular hardware configuration, thepresent invention is typically implemented in software by a system 40,shown in FIG. 7, containing a computer 42 in communication with ananalytical instrument, in this case a LC-MS instrument 44 that includesa liquid chromatography instrument 46 connected to a mass spectrometer48 by an interface 50. The computer 42 acquires raw data directly fromthe instrument 44 via an analog-to-digital converter. Alternatively, theinvention can be implemented by a computer in communication with aninstrument computer that obtains the raw data. Of course, specificimplementation details depend on the format of data supplied by theinstrument computer. Preferably, the entire process is automated: theuser sets the instrument parameters and injects a sample, thetwo-dimensional data are acquired, and the data are filtered forsubsequent processing or transfer to a suitable database.

The computer 42 implementing the invention typically contains aprocessor 52, memory 54, data storage device 56, display 58, and inputdevice 60. Methods of the invention are executed by the processor 52under the direction of computer program code stored in the computer 42.Using techniques well known in the computer arts, such code is tangiblyembodied within a computer program storage device accessible by theprocessor 52, e.g., within system memory 54 or on a computer readablestorage medium 56 such as a hard disk or CD-ROM. The methods may beimplemented by any means known in the art. For example, any number ofcomputer programming languages, such as Java, C++, or LISP may be used.Furthermore, various programming approaches such as procedural or objectoriented may be employed.

It is to be understood that the steps described above are highlysimplified versions of the actual processing performed by the computer42, and that methods containing additional steps or rearrangement of thesteps described are within the scope of the present invention.

It should be noted that the foregoing description is only illustrativeof the invention. Various alternatives and modifications can be devisedby those skilled in the art without departing from the invention.Accordingly, the present invention is intended to embrace all suchalternatives, modifications and variances which fall within the scope ofthe disclosed invention.

1. Apparatus for characterizing a chemical sample, comprising: achromatography instrument operatively associated with a massspectrometer; and a processor comprising means for obtaining achromatogram comprising signal data and noise date from a series of massspectra of said sample produced by said chromatography instrument andsaid mass spectrometer, said processor further comprising means forapplying a median filter to said chromatogram to remove at least some ofsaid noise data, thereby producing a filtered chromatogram.
 2. Theapparatus of claim 1, wherein said chromatogram is a total ionchromatogram and said filtered chromatogram is a filtered total ionchromatogram.
 3. The apparatus of claim 2, wherein said processorfurther comprises means for generating individual chromatograms fromsaid total ion chromatogram, wherein said median filter is applied tosaid individual chromatograms.
 4. The apparatus of claim 1, wherein saidmedian filter is a moving median filter.
 5. The apparatus of claim 1,wherein said median filter is a modified median filter.
 6. The apparatusof claim 1, wherein said processor further comprises means for selectinga parameter of said median filter in dependence on a scan rate of saidmass spectrometer.
 7. The apparatus of claim 1, wherein said processorfurther comprises means for selecting a parameter of said median filterin dependence on subsequent data analysis of said mass spectra.
 8. Theapparatus of claim 7, wherein said subsequent data analysis comprisespeak selection.
 9. The apparatus of claim 2, wherein said processorfurther comprises means for performing a component detection analysis onsaid filtered total ion chromatogram.
 10. The apparatus of claim 1,wherein said chromatography instrument is a liquid chromatographyinstrument.